İşgücü Yönetiminde Sezgiseller: Geliştirilmiş Deve Algoritmasının Bir Uygulaması

Yıl 2024, Cilt: 11 Sayı: 1, 37 - 48, 30.06.2024

Mehmet Fatih Demiral

https://doi.org/10.34232/pjess.1498652

Öz

Geliştirilmiş Deve Algoritması (MCA), 2016, 2019 ve 2021 yıllarında mühendislik problemlerine uygulanan zorlu bir algoritmadır. MCA, diğer tekniklere kıyasla işletme, ekonomi, işgücü yönetimi ve bilim alanlarına uygulanabilir. Saf MCA, literatürdeki optimizasyon problemlerini etkili ve oldukça hızlı çözer. Geliştirilmiş deve algoritmasını kullanarak iş gücü yönetiminde matematiksel modeli geliştirmek ve uygulamak için bu algoritma, tur oluşturma yöntemi (MC) gibi popüler buluşsal yöntemle birleştirildi ve daha sonra örneğin 2-opt, 3-opt ve k-opt gibi yerel aramalarla iyileştirildi. Önerilen hibrit algoritmalar uygun parametreler altında test edilmiştir. Deneysel çalışmada düzgün dağılım sağlanarak rastgele model veri setleri ve uygun parametreler kullanılmıştır. Deneysel sonuçlar örnek verisetleri ve uygun parametreler için en iyi, ortalama, standart sapma ve CPU zamanı olarak verilmiştir. Özet olarak, önerilen hibrit meta-sezgiseller, örnek rastgele veri kümeleri için kabul edilebilir CPU zamanında makul iş gücü yönetimi çözümleri bulmaktadır.

Anahtar Kelimeler

Atama problemi, Geliştirilmiş Deve Algoritması, İşgücü Yönetimi, Metasezgiseller, Sezgiseller

Etik Beyan

Bu araştırma etik kurallar çerçevesince hazırlanmıştır.

Heuristics in Labor Management: An Application of Modified Camel Algorithm

Öz

Modified Camel Algorithm (MCA) is a challenging algorithm applied to engineering problems in 2016, 2019, and 2021. MCA can be implemented to the field of business, economics, labor management, and science compared to the other techniques. The pure MCA solves optimization problems effectively and quite fast in literature. To develop and apply the mathematical model in labor management using the modified camel algorithm, it was combined with popular heuristics, such as constructive heuristic (MC), and then improved with local searches, for instance 2-opt, 3-opt, and k-opt. The suggested hybrid algorithms are tested under proper parameters. In the experimental study, random model datasets, and suitable parameters are used via uniform distribution. The experimental outcomes are given as best, average, std. deviation and CPU time for sample datasets with proper parameters. In short, the suggested hybrid metaheuristics find reasonable solutions of labor management in acceptable CPU time for all random datasets.

Anahtar Kelimeler

Assignment problem, Heuristics, Labor Management, Metaheuristics, Modified Camel algorithm

Kaynakça

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